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联合学习多种疾病眼科诊断使用OCT血管学

Ahammed Sakir Nabil¹, Sina Gholami¹, Theodore Leng²

¹Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, North Carolina.

Ophthalmology science

|February 24, 2026

概括

联合学习 (FL) 策略显示了使用OCT血管学 (OCTA) 进行多种疾病视网膜分类的前景,实现了与集中方法相比的性能,同时保持了患者的隐私.

科学领域:

眼科医生眼科眼科
医疗成像医学成像
人工智能的人工智能

背景情况:

使用OCT血管学 (OCTA) 进行视网膜疾病分类对于诊断和治疗至关重要.
联合学习 (FL) 为多机构数据分析提供了一种保护隐私的方法.
在优化 FL 针对异质医疗数据方面存在挑战.

研究的目的:

用OCT血管学 (OCTA) 系统评估用于多种疾病视网膜分类的联合学习 (FL) 策略.
在现实的异质条件下建立基础可行性并优化FL性能.
确保在多机构的OCTA数据分析中保护隐私.

主要方法:

一个追溯的多中心FL研究,具有2部分实验设计 (同质和异质条件).
评估了五种FL聚合策略 (FedAvg,FedProx,FedMRI,FedAdagrad,FedYogi) 在各种架构,转移学习和局部时代配置中.
整合和评估安全机制,如差异隐私和安全聚合.

主要成果:

在简化分类方面,FL在集中培训中取得了优异的成绩 (72.09%的准确率).
采用特定结策略的DenseNet121架构优化了性能 (79.55%的精度,89.68%的ROC-AUC).
联合的近接显示了对异质性的弹性;安全的聚合平衡了隐私和实用性.

结论:

联合学习 (FL) 为基于OCTA的多机构疾病分类提供了一个全面的,保护隐私的解决方案.
优化的FL策略可以与集中式方法相匹配或超过.
仔细选择架构,优化和安全机制是临床实用性和监管合规性的关键.

关键词:

与年龄相关的黄斑变性 (AMD)糖尿病视网膜病变 (DR) 是一种联合学习是联合学习.光学连贯断层扫描血管学 (OCTA)保护隐私的人工智能保护人工智能

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